Abstract
In recent years, with technical improvements and growth in content-based advertising, individuals have started utilizing SMS (Short Message Service), which has resulted in a significant increase in spam SMS. African continent experiences the most spam SMS in the globe, with an average of 119 spam SMS received per month by a person. These spam SMS are unsolicited messages to users, which are disturbing and may sometimes lead to the loss of important data. There exist many spam SMS detection methods which are impacted by the inclusion of well-known words, phrases, abbreviations, and idioms. The proposed work is using supervised machine learning techniques such as Multinomial Naïve Bayes and Support Vector Machine to identify SMS as spam or ham and compare their results based on specific evaluation parameters to find the most effective technique for filtering messages. The technique is evaluated on a real-world SMS dataset including over 5572 messages. According to the findings, the Support Vector Machine algorithm is the best at classifying SMS as spam or ham with an accuracy of 98.83%.
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Patil, L., Sakhidas, J., Jain, D., Darji, S., Borhade, K. (2023). A Comparative Study of Spam SMS Detection Techniques for English Content Using Supervised Machine Learning Algorithms. In: Thampi, S.M., Mukhopadhyay, J., Paprzycki, M., Li, KC. (eds) International Symposium on Intelligent Informatics. ISI 2022. Smart Innovation, Systems and Technologies, vol 333. Springer, Singapore. https://doi.org/10.1007/978-981-19-8094-7_16
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DOI: https://doi.org/10.1007/978-981-19-8094-7_16
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